Implementation and investigation of a reservoir computer based on a hardware model of three-element spiking neuron

This paper investigates new computer architectures for the hardware implementation of dynamic (spiking) neural networks capable to replace up-to-date networks built on neurons with a static activation function. We propose for the first time the use of a recently developed compact analog model of a spiking neuron, consisting of only three elements (a volatile memristor, a tunnel diode, and a capacitor), as the basic element of a reservoir computer of Liquid State Machine (LSM) type. A computer model of the reservoir is proposed, including 7,480 neurons and approximately 254,000 connections, with a topology formed using the biologically motivated LSM stochastic synapse distribution algorithm. The results of the proposed solution are demonstrated on the task of recognizing handwritten digits from the MNIST dataset. A classification accuracy of 93 % is achieved, which is comparable to known LSM implementations. Estimates for the proposed reservoir performance of the future hardware implementation exceed those of existing analogs by an order, and in terms of energy efficiency by 3-4 orders. Thus, the proposed study demonstrates for the first time the practical applicability of the three-element neuron model for machine learning tasks and confirms its potential as a basic element for constructing scalable and energy-efficient neuromorphic computing systems.

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